In the rapidly evolving realm of cloud computing security, this paper introduces an innovative solution to address persistent challenges. The proliferation of cloud technology has brought forth heightened concerns regarding data security, necessitating novel approaches to safeguarding sensitive information. The issue centers on the vulnerability of cloud-stored data, often necessitating enhanced encryption and key management strategies. Traditional methods often fall short in mitigating risks associated with compromised encryption keys and centralized key storage. To combat these challenges, our proposed solution encompasses a two-phase approach. In the first phase, dynamic Advanced Encryption Standard (AES) keys are generated, ensuring each file’s encryption with a unique and ever-changing key. This approach significantly enhances file-level security, curtailing an attacker’s ability to decrypt multiple files even if a key is compromised. The second phase introduces blockchain technology, where keys are securely stored with accompanying metadata, bolstering security and data integrity. Elliptic Curve Cryptography (ECC) public key encryption enhances security during transmission and storage, while also facilitating secure file sharing. In conclusion, this comprehensive approach enhances cloud security, providing robust encryption, decentralized key management, and protection against unauthorized access. Its scalability and adaptability make it a valuable asset in contemporary cloud security paradigms, assuring users of data security in the cloud.

This comprehensive study investigates the integration of cloud computing and deep learning technologies in medical data analysis, focusing on their combined effects on healthcare delivery and patient outcomes. Through a methodical examination of implementation instances at various healthcare facilities, we investigate how well these technologies manage a variety of medical data sources, such as wearable device data, medical imaging data, and electronic health records (EHRs). Our research demonstrates significant improvements in diagnostic accuracy (15–20% average increase) and operational efficiency (60% reduction in processing time) when utilizing cloud-based deep learning systems. We found that healthcare organizations implementing phased deployment approaches achieved 90% successful integration rates, while hybrid cloud architectures improved regulatory compliance by 50%. This study also revealed critical challenges, with 35% of implementations facing data integration issues and 5% experiencing security breaches. Through empirical analysis, we propose a structured implementation framework that addresses these challenges while maintaining high performance standards. Our findings indicate that federated learning techniques retain 95% model accuracy while enhancing privacy protection, and edge computing reduces latency by 40% in real-time processing. By offering quantitative proof of the advantages and difficulties of combining deep learning and cloud computing in medical data analysis, as well as useful recommendations for healthcare organizations seeking technological transformation, this study adds to the expanding body of knowledge on healthcare digitalization.

In the context of cloud systems, the effectiveness of placing modules for optimal reliability and end-to-end delay (EED) is directly linked to the success of scheduling distributed scientific workflows. However, the measures used to evaluate these aspects (reliability and EED) are in conflict with each other, making it impossible to optimize both simultaneously. Thus, we introduce a scheduling algorithm for distributed scientific workflows that focuses on enhancing reliability while maintaining specific EED limits. This is particularly important given the inevitable failures of processing servers and communication links. To achieve our objective, we first develop an artificial intelligence-based model that merges an improved version of the wild horse optimization technique with a levy flight approach. This hybrid approach enhances the ability to explore new possibilities effectively. Additionally, we establish a viable strategy for sharing mapping decisions and stored information among processing servers, promoting scalability and robustness—essential qualities for large-scale distributed systems. This strategy not only boosts local search capabilities but also prevents premature convergence of the algorithm. The primary goal of this study is to pinpoint resource placements that strike a balance between global exploration and local exploitation. This entails effectively harnessing the search space and minimizing the inclination toward resources with a high likelihood of failures. Through experimentation in various system configurations, our proposed method consistently outperformed competing workflow scheduling algorithms. It achieved notably higher levels of reliability while adhering to the same EED constraints.